Machine learning is a technical term which refers to the process where a computer learns more ways, over time, to complete a task using data, without having to be reprogrammed. It is a process that scientists and computer experts are all striving to bring to life – and soon.

Machine learning is rapidly changing, turning out to be the ultimate go-to for predictive calculations for data scientists and software developers. Scientists want to tap into the neural network of a computer to unlock its full potential, and for that they are making use of Microsoft Azure ML Studio as their ultimate tool of choice. Azure offers a faster learning period for coding combinations, along with special azure training to get their working started immediately.

Microsoft Azure Machine Learning Studio

Microsoft Azure’s Machine Learning Studio is essentially a tool that uses simple drag and drop options in order to assist you in building, testing, and running all the predictive analysis and its solutions, related to your specific data.

This particular instrument further helps you publish proper models which work as web services, in order to be consumed by apps that could be customized as per individual requirements. It also enables personalization with the help of BI tools, such as, Microsoft Excel.

Azure comes with algorithms to run predictive analytics solutions for learning categories like regression, classification, cluttering, and spotting the anomaly, to name a few.

In order to help you choose the most appropriate learning algorithm for your work, a cheat sheet is also available along with Microsoft Azure Training.

Getting Started

Using Microsoft Azure Machine Learning Studio is easy, and the readily available Microsoft Azure Machine Learning cheat sheet makes the process even easier.

As you enter the Microsoft Azure Machine Learning studio for the first time, you’re introduced to the basic home page. With a simplified interface, the home page allows you easy and immediate access to the documentation, videos, webinars, and all other necessary resources.

Moving on from the home page, you venture onto the second page, which is the Studio. Accessible only with specific log-in details, the second page requests you to sign in with your Microsoft credentials in order to view the available list of options.

Once signed in, the options available are as follows:

Projects

It is a chosen set of different experiments and data along with other resources which are used for a particular project.

Experiments

This portion is dedicated to all of your experiments. Every experiment that you run is saved here as a draft, allowing you to change your variables in any experiment as needed.

Web Services

A list of your web services that your experiments deployed are available here. There is also a provision of saving your current work in order to view the results at a later time or date, should you so desire.

Notebooks

The Notebooks available here are specific Jupyter Notebooks which have been created especially for Microsoft Azure Machine Learning. This section is dedicated to notebooks created by you.

Data Sets

For each of your experiment and predictive analytics solution you are required to provide the learning machine with some data. The Data Sets portion is your place to upload data for your studio.

Trained Models

Models that are created from your experiments and are working on your data sets are saved in this section.

Settings

This particular section is a complete collection for the settings that can be used to customize your studio, resources, and your account.

The webpage also has a Gallery, which contains different Azure AI as a collection. Furthermore, an Azure machine learning certification course is also available online for anyone wanting to learn how to operate the Studio. This is the webpage where developers and scientists put up their solutions that are created by using the different tools available.

How To: Experiments

As a new user, it is important to understand how to create experiments right from scratch. Once in the Experiments section, you are required to either choose between templates based on all sorts of data sets used previously, or you can create one of your own to begin with.

Experiment usually consists of various datasets providing the required information to run your work. A valid experiment:

Has a data set and module

Has dataset connected to modules only

Contains modules that are connected to datasets and other modules

Has input of module with a connection for data flow

Should have the required parameters set before hand for each module

Start with collecting data by either creating your own or selecting from a collection source. In case of the latter, you could simply drag the data set to creating a proper Training Experiment Canvas

Screen the data and remove any missing portions in your data, if there are any. You can do that by selecting ‘Column’ in the Dataset modules. Similarly, remove any row with missing data.

Observe the data and select the data columns with the data you want to use to create a prediction. You can do this by selecting the columns in the dataset module.

Divide the data into two sets, one as a set for training and one as a testing set. Do this by using the option of Split Data module.

Choose from the available algorithms and apply them to your data. You can choose from the many different algorithms available, applying different algorithms based on your type of data and the types of prediction.

Use a training set and train your model. A training model uses the majority of data in the split.

Score the model with testing set. A test model use the minority of the data from the split.

Run tests for the quality of results. Using Evaluate Model modules.

Keep repeating in order to improve the model.

Add branches to your experiments, creating more experiments.

Choose the experiment with the best models, and create a Predictive Experiment.

Put up a Predictive Experiment as a proper web service.

How To Create your own Azure Machine Learning

Create A New Experiment

Microsoft Azure has provided the perfect tool for Learning Machines, proffering an easy to use platform for IT professionals and novice enthusiasts from around the world. By providing a comprehensive online Azure training program, Microsoft further facilitates its users by bridging the gaps between every day lives and the cutting-edge technologies of artificial intelligence. This, combined with the instrument itself, offers a one of a kind learning experience, allowing IT professionals and hopefuls to exponentially elevate their standards and skills.

Faisal has a decade of experience in leading IT teams in the areas of Development, Infrastructure and DevOps. He and his team helped move the organization from On-Premises to AWS Cloud platform. He played a key role in architecture of AWS Platform. With his years of development experience, he and his team made the groundbreaking workforce readiness platform (CLIPP).